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      The skin microbiota of the axolotl Ambystoma altamirani is highly influenced by metamorphosis and seasonality but not by pathogen infection

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          Abstract

          Background

          Microbiomes have been increasingly recognized as major contributors to host health and survival. In amphibians, bacterial members of the skin microbiota protect their hosts by inhibiting the growth of the fungal pathogen Batrachochytrium dendrobatidis (Bd). Even though several studies describe the influence of biotic and abiotic factors over the skin microbiota, it remains unclear how these symbiotic bacterial communities vary across time and development. This is particularly relevant for species that undergo metamorphosis as it has been shown that host physiology and ecology drastically influence diversity of the skin microbiome.

          Results

          We found that the skin bacterial communities of the axolotl A. altamirani are largely influenced by the metamorphic status of the host and by seasonal variation of abiotic factors such as temperature, pH, dissolved oxygen and conductivity. Despite high Bd prevalence in these samples, the bacterial diversity of the skin microbiota did not differ between infected and non-infected axolotls, although relative abundance of particular bacteria were correlated with Bd infection intensity.

          Conclusions

          Our work shows that metamorphosis is a crucial process that shapes skin bacterial communities and that axolotls under different developmental stages respond differently to environmental seasonal variations. Moreover, this study greatly contributes to a better understanding of the factors that shape amphibian skin microbiota, especially in a largely underexplored group like axolotls (Mexican Ambystoma species).

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s42523-022-00215-7.

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          Most cited references94

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              Analysis of composition of microbiomes: a novel method for studying microbial composition

              Background Understanding the factors regulating our microbiota is important but requires appropriate statistical methodology. When comparing two or more populations most existing approaches either discount the underlying compositional structure in the microbiome data or use probability models such as the multinomial and Dirichlet-multinomial distributions, which may impose a correlation structure not suitable for microbiome data. Objective To develop a methodology that accounts for compositional constraints to reduce false discoveries in detecting differentially abundant taxa at an ecosystem level, while maintaining high statistical power. Methods We introduced a novel statistical framework called analysis of composition of microbiomes (ANCOM). ANCOM accounts for the underlying structure in the data and can be used for comparing the composition of microbiomes in two or more populations. ANCOM makes no distributional assumptions and can be implemented in a linear model framework to adjust for covariates as well as model longitudinal data. ANCOM also scales well to compare samples involving thousands of taxa. Results We compared the performance of ANCOM to the standard t-test and a recently published methodology called Zero Inflated Gaussian (ZIG) methodology (1) for drawing inferences on the mean taxa abundance in two or more populations. ANCOM controlled the false discovery rate (FDR) at the desired nominal level while also improving power, whereas the t-test and ZIG had inflated FDRs, in some instances as high as 68% for the t-test and 60% for ZIG. We illustrate the performance of ANCOM using two publicly available microbial datasets in the human gut, demonstrating its general applicability to testing hypotheses about compositional differences in microbial communities. Conclusion Accounting for compositionality using log-ratio analysis results in significantly improved inference in microbiota survey data.
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                Author and article information

                Contributors
                rebollar@ccg.unam.mx
                Journal
                Anim Microbiome
                Anim Microbiome
                Animal Microbiome
                BioMed Central (London )
                2524-4671
                12 December 2022
                12 December 2022
                2022
                : 4
                : 63
                Affiliations
                [1 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Centro de Ciencias Genómicas, , Universidad Nacional Autónoma de México, ; Cuernavaca, Mexico
                [2 ]GRID grid.412872.a, ISNI 0000 0001 2174 6731, Instituto de Ciencias Agropecuarias y Rurales, , Universidad Autónoma del Estado de México, ; Toluca, Mexico
                [3 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Facultad de Ciencias, , Universidad Nacional Autónoma de México, ; Mexico City, Mexico
                [4 ]GRID grid.251313.7, ISNI 0000 0001 2169 2489, Department of Biology, , University of Mississippi, ; Oxford, MS USA
                Article
                215
                10.1186/s42523-022-00215-7
                9743558
                36503640
                1265974a-150d-4347-9e87-20eb886ac204
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 April 2022
                : 16 October 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                skin microbiota,amphibians,metamorphosis,seasonality
                skin microbiota, amphibians, metamorphosis, seasonality

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